Efficient World Models with Context-Aware Tokenization

Efficient World Models with Context-Aware Tokenization

The paper "Efficient World Models with Context-Aware Tokenization" presents a novel approach to addressing the computational inefficiencies of reinforcement learning (RL) agents operating in visually complex environments. The paper proposes a new world model architecture, denoted as \diris, which leverages a discrete autoencoder for encoding stochastic deltas between time steps and an autoregressive transformer that predicts future deltas by summarizing the current state with continuous tokens.

Key Contributions

The main contributions of the paper can be summarized as follows:

1. Discrete Autoencoder with Contextual Encoding: The authors introduce an autoencoder that conditions its encoding on past frames and actions. This design effectively captures the stochastic elements of the deltas between frames, rather than encoding entire frames independently. This results in a significant reduction in the number of tokens required for encoding a sequence.
2. Autoregressive Transformer with Continuous Tokens: To address the difficulty of reasoning over multiple time steps solely based on previous discrete delta tokens, the paper interleaves continuous tokens (\itoken-tokens) with discrete tokens (\dtoken-tokens) in the sequence processed by the autoregressive transformer. This hybrid sequence improves the transformer’s ability to model the dynamics by providing richer context at each prediction step.

Experimental Evaluation

The proposed \diris\ model is evaluated against multiple baselines, including versions of DreamerV3 and iris. The experiments are conducted on the Crafter benchmark, which requires handling high-dimensional visual observations and complex, stochastic dynamics. The \diris\ model achieves state-of-the-art performance, solving 17 out of 22 tasks after 10 million frames of data collection while being computationally efficient. Notably, \diris\ is an order of magnitude faster to train compared to previous attention-based methods like iris.

Detailed Insights

Autoencoder Conditioning on Past Frames

The core novelty in \diris\ lies in the conditioning mechanism of its autoencoder. By attending to past observations and actions, the autoencoder can compactly represent only the stochastic changes between frames. This approach drastically reduces the computational burden on the transformer model. Empirical evaluations indicate that \diris\ can reconstruct frames with as few as four tokens while maintaining high fidelity, compared to up to 64 tokens required by iris for similar performance.

Continuous and Discrete Token Interleaving

The paper highlights the challenge that even powerful autoregressive models face when needing to integrate over extended sequences of discrete tokens to form a current world representation. By introducing continuous \itoken-tokens into the sequence, \diris\ mitigates the issue, allowing the transformer to leverage a more context-rich, mixed representation to predict future states. Experimental results show that removing the \itoken-tokens significantly degrades the model's performance, validating the necessity of this design.

Implications and Future Work

The \diris\ model presents practical implications for reinforcement learning in high-dimensional and visually complex environments. Its efficiency in both computational overhead and training time makes it a viable candidate for real-world applications where interaction with the environment is costly or risky.

From a theoretical perspective, \diris\ exemplifies how combining discrete and continuous representations can enhance the capability of sequence models to capture environment dynamics effectively. This principle could be extended beyond RL to other domains involving sequential data.

Future developments could explore variable-length token sequences, where the number of tokens is dynamically adjusted based on the current context to further optimize computational efficiency. Additionally, leveraging the internal representations of the world model for policy learning could yield more robust and lightweight RL agents.

Conclusion

This work advances the field of model-based reinforcement learning by introducing a method that balances the need for high representational power with computational efficiency. Through the effective use of context-aware tokenization and mixed discrete-continuous sequences, the authors set a new benchmark in the Crafter environment while reducing training time significantly. These innovations set the stage for more scalable and efficient application of RL in complex real-world scenarios.